Agent adaptability — the ability of agents to change behavioral strategies when it is beneficial to do so — is presumed to be an important part of the robustness of complex adaptive systems (CAS). But, determining when changing behaviors is advantageous for agents has proven quite challenging in CAS research, as sometimes behavioral change is necessary, but other times it can impose costs that exceed benefits. I present the results from experiments using an agent-based model (ABM) designed to discover thresholds after which behavioral flexibility leads to improved societal-level outcomes in groups of agents in dynamic environments. The first major result is that there are thresholds in both levels of flexibility in agent behavior and in levels of turbulence in the environment below and above which there are marked differences in utility gains for agents. In particular, relatively high flexibility leads to lower overall utility scores, as well as, surprisingly, decreased diversity and increased inequality between agents. The second result is that at very high levels of environmental turbulence, the effects of the environment alone on agent utility overshadow any benefits to agents from flexible behavior strategies. This suggests, counter-intuitively, that the best strategy for agents in very dynamic environments is simply to keep behavior constant. The third major result is that there is an interaction between agent behavior and the environment: high flexibility of other agents can effectively make an environment more "dynamic", which just fuels more flexibility, and leads to a scramble between different strategies with no utility gain. A final theoretical contribution of the paper is that the model is able to show drawbacks to flexibility without relying on costs to changing behavior, as is done in much of the literature on strategy change.

The present historical epoch is unique in the sense that now The present work takes a shot at this target. The author's people may have the opportunity to create something equal answer to the first question should be clear from the above to them, if not greater: machines capable of humanlike and can be formulated concisely as follows: the goal is to intellectual and cultural development. The reason is not design a human-level learner. Yet, this statement needs a only that the hardware available today is compatible in its further clarification. Its limited interpretation could be, raw computational capacities with the human brain. The e.g.: "The goal of a human-level learner is to take complex, main reason is the emergent understanding of how the noisy information from multiple modalities and distill this human mind works. It appears that implementing the same experience into a representation that supports prediction principles of the human mind in a machine would not take about and manipulation of the world" (Shrobe et al., 2006, yet unavailable today computer resources.

Argumentation is a form of reasoning that makes explicit the reasons for the conclusions that are drawn and how con- flicts between reasons are resolved. This provides a natural mechanism, for example, to handle inconsistent and uncer- tain information and to resolve conflicts of opinion between intelligent agents. The advantage of a mechanism based on argumentation is that considering the reasons behind the conclusions offers more than considering the conclusions alone (to adapt something Isaac Bashevis Singer once said, the approach has “more vitamins” than other approaches to reasoning). For example, in dealing with inconsistent infor- mation, an early use of argumentation, it is possible to know more than just that we have the inconsistent conclusions p and not p. We can establish exactly which pieces of infor- mation lead to these conclusions and can then prioritize one conclusion over another on the basis of this information, de- cide what information should be revised to achieve consis- tency, or even determine what additional investigation needs to be carried out (when we have reason to believe both that it is raining outside and not raining outside, and have no way of determining which is correct, going to look may be the best solution).

Issues concerning students involved with online learning paths, that need to be faced by e-Tutors on their day-to-day activity, most often than not fall into known pedagogical patterns - that are problems and difficulties already occurred in the past and dealt with. These pedagogical patterns belong to e-Tutors' know-how and experience and their resolution are frequently a matter of activating routine processes or giving  pre-factored answers; nevertheless statistical data indicates that these issues consume a considerable slice of tutors' time. While a portion of the scientific community is still devoting much effort in developing artificial tutoring systems - by deploying AI/MAS-enabled technologies - the solution being investigated by our team focuses on enhancing already-available, open source LMS by implementing a general-purpose tracking and monitoring toolkit able to support e-Tutors in recognizing and dealing with pedagogical patterns stored into a decentralised Knowledge Base. The system architecture is designed to house multiple platforms (only one adapter interface needs to be written for each LMS) and is able to perform real-time, as well as scheduled, data collection by means of Jade-based agents and schedulers.  Information obtained from the processed data is then returned to the platform via web services and specific interfaces (instant messaging chatbot). The first deployed prototype is currently being experimented in adult higher education learning paths and is able to track student activity, forum readings and writings and offers a basic chat-based help interface. Our aim is to turn a standard LMS into a knowledge aggregator where information about its users, its contents and interactions between the two can be mined via Knowledge Services; resulting data could then be used to refine users' and groups' profiles, to monitor learners' deviance from expected learning path, and ultimately to adjust the applied pedagogical model.

In this work we will present an integration of a query-answering argumentation approach with an abstract agent programming language. Agents will argumentatively reason via queries, using information of their mental components. Special context-based queries will be used to model the interaction between mental components. Deliberation and execution semantics of the proposed integration are presented.

This paper describes some functionalities and features of the Maryland Virtual Patient (MVP) environment. MVP models the process of disease progression, diagnosis and treatment in virtual patients who are endowed with a “body,” a simulation of their physiological and pathological processes, and a “mind,” a set of capabilities of perception, reasoning and action that allow the virtual patient to exhibit independent behavior, participate in a natural language dialog, remember events, hold beliefs about other agents and about specific object and event instances, make decisions and learn.

The coagulation system (CS) is a complex, inter-connected biological system with major physiological and pathological roles. The CS may be viewed as a complex adaptive system, in which individual components are linked through multiple feedback and feedforward loops. The non-linear relationships between the numerous coagulation factors and the interplay among the elements of the CS render the study of this biology at a molecular and cellular level nearly impossible. We present an Agent Based Modeling and Simulation (ABMS) approach for simulating these complex interactions. Our ABMS approach utilizes a subset of 52 rules to define the interactions among 33 enzymes and factors of the CS. These rules simulate the interaction of each “agent”, such as substrates, enzymes, and cofactors, on a two-dimensional grid of ~12,000 cells and ~300,000 agents. Our ABMS method successfully reproduces the initiation, propagation, and termination of thrombin formation due to the activation of the extrinsic pathway. Furthermore, the ABMS is able to demonstrate the emergence of a threshold effect for thrombin generation as a result of the synergistic effect of combining anticoagulant systems.

The relationship between equilibrium convergence to a uniform quality distribution and price is investigated in the Q-model, a self-organizing, evolutionary computational matching model of a fixed-price post-secondary higher education created by Ortmann and Slobodyan (2006). The Q-model is replicated with price equaling 100% its Ortmann and Slobodyan (2006) value, Varying the fixed price between 0% and 200% reveals thresholds at which the Q-model reaches different market clustering configurations. Results indicate structural market robustness to prices less than 100% and high sensitivity to prices greater than 100%.

Assumption-Based Argumentation (ABA), and to a large extent argumentation in general, up to now has been considered in a single-agent setting. ABA, in particular, is such that an agent engages in a dispute (dialectic proof procedure) with itself (an imaginary opponent) to decide whether a claim is acceptable according to some acceptability criteria. We present in this paper a generalised proof procedure for the admissibility semantics of ABA, which is still a dispute by an agent with itself but such that the outcome can be readily communicated to other agents. This is important for applications in multi-agent systems wherein agents may differ in the knowledge they have and may need to communicate their arguments between one another to convince each other of the acceptability or not of a given claim.

Boid flocking is a system in which several individual agents follow three simple rules to generate swarm-level flocking behavior. To control this system, the user must adjust the agent program parameters, which indirectly modifies the flocking behavior. This is unintuitive because the properties of the flocking behavior are non-explicit in the agent program. In this paper, we discuss a domain-independent approach for detecting and controlling two emergent properties of boids: density and a qualitative threshold effect of swarming vs. flocking. Also, we discuss the possibility of applying this approach to detecting and controlling traffic jams in traffic simulations.